Di-methyl-Histone H3(K36) Monoclonal Antibody

What is Di-methyl-Histone H3(K36) and what is its role in chromatin biology?

Di-methyl-Histone H3(K36) represents a specific post-translational modification where two methyl groups are attached to the lysine residue at position 36 of histone H3. Contrary to earlier models suggesting H3K36me2 was merely an intermediate state, recent research demonstrates it represents an independent chromatin state with distinct functions . This modification plays crucial roles in various nuclear processes including transcriptional regulation, chromatin structure maintenance, and DNA repair pathway selection. H3K36me2 is particularly important for recruiting specific reader proteins that execute downstream functional effects on chromatin. The dimethylation state creates a specific biochemical interaction surface that allows recognition by domains such as PWWP, chromo, and tudor domains found in various chromatin-associated proteins . In the context of chromatin biology, H3K36me2 often marks specific genomic regions, including intergenic regions and certain heterochromatic loci, creating functionally distinct domains from those marked by H3K36me3 .

How does Di-methyl-Histone H3(K36) differ from mono- and tri-methylation states?

Di-methyl-Histone H3(K36) represents a distinct epigenetic state from mono- and tri-methylation at the same residue, with each methylation state associated with different genomic contexts and biological functions:

This distinction contradicts the prevailing linear model where K36me1/2 were considered merely as methylation intermediates toward K36me3 . Research has demonstrated that these states exist independently and serve as platforms for recruiting different effector proteins that execute distinct biological functions .

Which enzymes are responsible for establishing Di-methyl-Histone H3(K36) marks?

Three primary methyltransferases regulate H3K36 methylation states in a context-dependent manner:

• NSD (Nuclear receptor SET Domain-containing protein): The primary enzyme responsible for establishing H3K36me2 marks. In Drosophila, NSD (orthologous to mammalian NSD1/2/3) places K36me2/3 at defined loci within pericentric heterochromatin and on weakly transcribed euchromatic genes . NSD primarily functions as a mono- and dimethyltransferase, laying the foundation for genome-wide H3K36 methylation patterns .

• Set2 (SET domain-containing 2): While primarily known for catalyzing H3K36me3 in active euchromatin, Set2 (orthologous to mammalian SETD2) can contribute to dimethylation under certain conditions. Set2 associates with elongating RNA polymerase II, establishing a link between transcription and H3K36 methylation states .

• Ash1 (Absent, small, or homeotic discs 1): Generally deposits H3K36me1 at regions with enhancer signatures, but can contribute to dimethylation in specific contexts. Ash1 (orthologous to mammalian ASH1L) functions independently of the Set2-NSD system and is developmentally regulated .

The interplay between these enzymes creates genomic context-dependent H3K36 methylation patterns that are crucial for proper chromatin function. Mutations in these enzymes, particularly NSD1, have been associated with various pathological conditions, including head and neck squamous cell carcinomas where approximately 20% exhibit reduced H3K36 methylation .

What are the recommended applications for Di-methyl-Histone H3(K36) monoclonal antibodies?

Di-methyl-Histone H3(K36) monoclonal antibodies serve as versatile tools across multiple experimental platforms in epigenetic research. These antibodies can be effectively utilized in the following applications:

These applications enable researchers to investigate both global levels and genomic distribution patterns of H3K36me2 marks, providing insights into their roles in chromatin organization, gene expression, and DNA repair processes . When selecting an antibody, researchers should consider specificity, host species (typically rabbit for Di-methyl-Histone H3(K36) antibodies), and validated positive controls such as HeLa or NIH/3T3 cell lines .

How should researchers optimize chromatin immunoprecipitation (ChIP) protocols using Di-methyl-Histone H3(K36) antibodies?

Optimizing ChIP protocols for Di-methyl-Histone H3(K36) antibodies requires careful consideration of several factors to maximize specificity and signal-to-noise ratio:

• Crosslinking Conditions: For H3K36me2 ChIP, standard formaldehyde fixation (1% for 10 minutes at room temperature) is typically sufficient, as this modification resides in the histone tail and is readily accessible. Over-fixation can mask epitopes, while under-fixation may lose important interactions.

• Chromatin Fragmentation: Aim for fragments between 200-500bp through sonication optimization. H3K36me2 is often found in intergenic regions and weakly transcribed euchromatic genes , so proper fragmentation is crucial for accurately mapping these distributions.

• Antibody Validation:

  • Perform peptide competition assays using modified and unmodified peptides

  • Test for cross-reactivity with other methylation states (H3K36me1/me3)

  • Include appropriate positive controls (regions known to be enriched for H3K36me2)

  • Include negative controls (IgG and regions lacking H3K36me2)

• Immunoprecipitation Conditions:

  • Use optimal antibody concentration (starting with 1:20 - 1:100 dilution)

  • Extend incubation time to 16 hours at 4°C to maximize specific binding

  • Carefully optimize wash stringency to remove non-specific interactions

• ChIP-qPCR Validation: Before proceeding to sequencing, validate enrichment at known H3K36me2-positive loci, focusing on intergenic regions and weakly transcribed genes where NSD deposits H3K36me2 .

• Sequential ChIP Considerations: For distinguishing readers that interact with both H3K36me2 and H3K36me3 (like MSL3 and JASPer) , sequential ChIP may be necessary to determine co-occupancy patterns.

• Bioinformatic Analysis: Account for genomic context in your analysis pipeline, as H3K36me2 patterns are highly context-dependent and influenced by the activity of three different methyltransferases .

What are the considerations for specificity when using Di-methyl-Histone H3(K36) antibodies in multi-omics approaches?

When integrating Di-methyl-Histone H3(K36) antibodies into multi-omics experimental designs, researchers must address several specificity considerations:

• Antibody Cross-Reactivity Evaluation:

  • Rigorously validate antibody specificity against mono- and tri-methylated H3K36 using dot blots with synthetic methylated peptides

  • Perform western blots comparing wildtype and methyltransferase knockout/knockdown samples (e.g., NSD-depleted cells)

  • Consider using recombinant antibodies with well-characterized epitope binding properties for improved reproducibility across experiments

• Multi-Modal Data Integration Challenges:

  • When combining ChIP-seq with transcriptomics, account for the context-dependent relationship between H3K36me2 and transcription

  • For proteomics integration, use antibody-based enrichment followed by mass spectrometry to identify H3K36me2-associated proteins

  • In spatial genomics applications, validate antibody performance in tissue sections with known H3K36me2 distribution patterns

• Reader Protein Dual Specificity:

  • Consider that some reader proteins (e.g., MSL3 and JASPer) recognize both H3K36me2 and H3K36me3

  • Design controls to distinguish reader recruitment driven by H3K36me2 versus H3K36me3

  • Use engineered systems with single methylation states to deconvolute reader specificity

• Genomic Context Interpretation:

  • Different genomic regions display distinct H3K36me2 patterns established by different methyltransferases

  • Intergenic regions and pericentric heterochromatin show NSD-dependent H3K36me2 enrichment

  • Weakly transcribed genes may exhibit both NSD and Set2-dependent H3K36me2

• Technical Compatibility Table for multi-omics approaches:

How does Di-methyl-Histone H3(K36) contribute to DNA double-strand break repair?

Di-methyl-Histone H3(K36) plays a critical and specific role in DNA double-strand break (DSB) repair that is distinct from the role of tri-methylated H3K36. Recent research has revealed that H3K36me2 specifically facilitates the non-homologous end joining (NHEJ) pathway of DSB repair, whereas H3K36me3 promotes homologous recombination (HR) . This methylation state-specific regulation represents a sophisticated epigenetic mechanism for directing DNA repair pathway choice.

The specific contribution of H3K36me2 to DSB repair involves several key mechanisms:

• Recruitment of NHEJ Machinery: H3K36me2 specifically recruits the yKu70 protein (in yeast) or its human homolog KU70 to DSB sites . This recruitment is critical for initiating the NHEJ repair pathway, as Ku70/80 heterodimers bind to DNA ends and prevent their degradation while facilitating the assembly of other NHEJ factors.

• Chromatin Context Sensing: H3K36me2 is enriched in intergenic regions, which often lack nearby homologous templates for HR repair . This enrichment pattern strategically positions NHEJ factors in genomic regions where NHEJ is the preferred repair mechanism.

• Repair Efficiency Regulation: Yeast cells lacking H3K36me2 exhibit reduced NHEJ efficiency, demonstrating the functional importance of this modification for proper DNA repair . This reduced efficiency increases sensitivity to DNA damaging agents and compromises genome stability.

• Pathway Choice Determination: The balance between H3K36me2 and H3K36me3 creates a binary switch mechanism that helps determine whether DSBs will be repaired by NHEJ or HR. This ensures that the appropriate repair pathway is utilized based on the chromatin context of the damage site .

Understanding the specific contribution of H3K36me2 to DSB repair has significant implications for cancer research, as dysregulation of repair pathway choice can lead to genomic instability and malignant transformation. Additionally, therapies targeting cells with altered H3K36 methylation patterns may exploit vulnerabilities in their DNA repair capabilities .

What is the relationship between Di-methyl-Histone H3(K36) and non-homologous end joining (NHEJ)?

The relationship between Di-methyl-Histone H3(K36) and non-homologous end joining (NHEJ) represents a direct functional connection mediated through specific protein-histone interactions:

• Direct Binding of NHEJ Factors: Research has demonstrated that yKu70 (in yeast) and its human homolog KU70 directly bind to H3K36me2-modified peptides and chromatin . This interaction is specific, as these factors show preferential binding to H3K36me2 over H3K36me3. The molecular basis for this specificity involves recognition of the dimethylated lysine by specialized domains within the Ku proteins.

• Recruitment Dependency:

  • When H3K36me2 is absent due to methyltransferase deficiency (particularly NSD), the recruitment of yKu70/KU70 to DSB sites is significantly impaired

  • This impaired recruitment directly correlates with reduced NHEJ efficiency

  • The dependency is specific, as other DSB repair pathways remain functional

• Genomic Context Influence: H3K36me2-enriched intergenic regions independently recruit yKu70 under DSB stress, creating repair-competent chromatin environments . This localization pattern establishes NHEJ-favorable domains throughout the genome that respond rapidly to DNA damage.

• Functional Consequences of Disruption:

  • Disrupting the interaction between yKu70/KU70 and H3K36me2 increases DNA damage sensitivity

  • Cells display decreased NHEJ repair efficiency when this interaction is compromised

  • The specificity of this interaction ensures appropriate repair pathway choice based on chromatin context

• Conservation of Mechanism: The exclusive association of human KU70 with H3K36me2 mirrors the pattern observed in yeast, indicating evolutionary conservation of this important regulatory mechanism . This conservation underscores the fundamental importance of H3K36me2 in NHEJ regulation across species.

This relationship provides a mechanistic explanation for how chromatin states influence DNA repair pathway choice and efficiency. By understanding how H3K36me2 facilitates NHEJ, researchers can better comprehend how epigenetic alterations might impact genome stability and potentially identify novel therapeutic approaches for diseases characterized by DNA repair deficiencies.

How can researchers differentiate between the roles of Di-methyl-Histone H3(K36) and Tri-methyl-Histone H3(K36) in DNA repair studies?

Differentiating between the roles of H3K36me2 and H3K36me3 in DNA repair requires sophisticated experimental approaches that can specifically manipulate and detect each methylation state:

• Methyltransferase-Specific Perturbations:

  • Selective knockdown/knockout of NSD to predominantly reduce H3K36me2 while minimally affecting H3K36me3

  • Selective inhibition of Set2/SETD2 to predominantly reduce H3K36me3

  • Compare repair outcomes between these conditions to distinguish methylation state-specific effects

• Site-Specific DNA Damage Induction:

  • Target DSBs to H3K36me2-rich regions (intergenic spaces) vs. H3K36me3-rich regions (gene bodies)

  • Use systems like CRISPR-Cas9 with guide RNAs targeting specific regions

  • Compare repair pathway choice and efficiency between these different chromatin contexts

• Methylation-Specific Reader Protein Analysis:

  • Generate mutations in reader proteins that disrupt binding to either H3K36me2 or H3K36me3 specifically

  • For readers like MSL3 and JASPer that bind both methylation states, perform structure-guided mutagenesis to create methylation state-specific binding variants

  • Monitor the impact of these mutations on repair pathway choice

• Sequential ChIP Approaches:

  • Perform sequential ChIP for H3K36me2 followed by repair factors (e.g., Ku70) and H3K36me3 followed by repair factors (e.g., Rfa1)

  • This technique can reveal which methylation state is directly associated with specific repair proteins at damage sites

• Histone Mutant Complementation:

  • Express histone H3 mutants that can only be methylated to specific states (e.g., K36R or K36A mutations complemented with appropriately mutated histones)

  • Analyze repair outcomes in these systems to isolate methylation state-specific effects

• Experimental Design Table for distinguishing H3K36me2 and H3K36me3 roles:

By implementing these approaches, researchers can effectively disentangle the specific contributions of H3K36me2 and H3K36me3 to DNA repair processes, advancing our understanding of how chromatin states influence genome maintenance mechanisms.

How are alterations in Di-methyl-Histone H3(K36) levels associated with cancer development?

Alterations in Di-methyl-Histone H3(K36) levels have emerged as significant epigenetic features in cancer development, with both mechanistic and clinical implications:

• Reduced H3K36me2 in Head and Neck Squamous Cell Carcinoma (HNSCC):

  • Approximately 20% of HNSCC exhibit reduced H3K36 methylation due to mutations in the histone methyltransferase NSD1 or direct mutations in histone H3 (H3K36M)

  • These alterations create distinct cancer subtypes with potentially different therapeutic vulnerabilities

  • HNSCC models with H3K36M mutations display variable phenotypes depending on the compensatory epigenetic changes

• Genome Instability Mechanisms:

  • Reduced H3K36me2 impairs non-homologous end joining (NHEJ) DNA repair, potentially contributing to genomic instability in cancer cells

  • The compromised recruitment of Ku70/Ku80 to DNA damage sites in H3K36me2-deficient cells may lead to error-prone repair and accumulation of mutations

• Interplay with Other Epigenetic Marks:

  • H3K36me2 reduction often leads to compensatory increases in H3K27me3, creating an altered epigenetic landscape

  • HNSCC with aberrant H3K27me3 accumulation due to H3K36M expression show decreased proliferation, increased genome instability, and higher sensitivity to genotoxic agents like PARP1/2 inhibitors

  • This represents a delicate balance between H3K36 and H3K27 methylation that is essential for maintaining genome stability

• Therapeutic Implications:

  • Cancer cells with H3K36M mutations and elevated H3K27me3 show increased sensitivity to PARP1/2 inhibitors

  • Those that maintain steady H3K27me3 levels can be sensitized to genotoxic agents by treatments that elevate H3K27me3, such as DNA hypomethylating agents or inhibitors of H3K27me3 demethylases KDM6A/B

• Biomarker Potential:

  • H3K36me2 levels may serve as biomarkers for predicting response to specific therapies

  • The ratio of H3K36me2 to H3K27me3 could potentially guide treatment selection for patients with HNSCC and other cancers

These associations highlight the importance of H3K36me2 in maintaining genome stability and suggest that its alteration represents a key event in carcinogenesis. Moreover, the epigenetic vulnerability created by reduced H3K36me2 offers potential therapeutic opportunities through synthetic lethality approaches targeting cells with these specific alterations.

What methodologies are recommended for studying Di-methyl-Histone H3(K36) in cancer models?

Studying Di-methyl-Histone H3(K36) in cancer models requires integrated approaches that span from molecular characterization to functional analysis:

These methodologies provide a comprehensive framework for investigating how alterations in H3K36me2 contribute to cancer development and for identifying potential therapeutic strategies targeting these epigenetic changes.

How can Di-methyl-Histone H3(K36) patterns serve as biomarkers in disease research?

Di-methyl-Histone H3(K36) patterns offer significant potential as biomarkers in disease research, particularly in cancer diagnostics, prognostics, and therapeutic stratification:

• Diagnostic Applications:

  • Cancer Subtype Classification: H3K36me2 patterns can distinguish between molecular subtypes of cancers, particularly in head and neck squamous cell carcinomas (HNSCC) where approximately 20% show reduced H3K36 methylation

  • Integration with Mutation Profiling: Combining H3K36me2 assessment with NSD1/2/3 mutation status provides a more complete diagnostic picture

  • Tissue-Specific Signatures: Different tissues exhibit distinct baseline H3K36me2 distributions; deviations from tissue-specific patterns may indicate pathological changes

• Prognostic Value Assessment:

  • Genome Stability Indication: Since H3K36me2 is critical for NHEJ repair , its reduction may predict increased genomic instability and potentially more aggressive disease

  • Epigenetic Balance Markers: The ratio of H3K36me2 to H3K27me3 serves as an indicator of epigenetic balance with prognostic implications

  • Longitudinal Monitoring Protocols: Serial liquid biopsy analysis of circulating nucleosomes for H3K36me2 levels may track disease progression

• Therapeutic Response Prediction:

  • DNA Damage Response (DDR) Therapy Response: H3K36me2-deficient tumors with elevated H3K27me3 show increased sensitivity to PARP1/2 inhibitors and potentially other genotoxic agents

  • Epigenetic Therapy Stratification: Tumors with low H3K36me2 but normal H3K27me3 levels may be sensitized to conventional therapies by treatment with H3K27me3 demethylase inhibitors

  • Combination Therapy Guidance: H3K36me2 status can inform rational combinations of epigenetic modifiers with conventional chemotherapy or radiotherapy

• Methodological Approaches for Biomarker Development:

• Implementation Considerations:

  • Antibody Selection: Use highly specific monoclonal antibodies for consistent detection across different laboratories and platforms

  • Reference Standards: Develop standardized positive and negative controls for H3K36me2 detection

  • Technical Validation: Ensure reproducibility through multi-center ring trials

  • Clinical Validation: Correlate H3K36me2 patterns with clinical outcomes in prospective studies

• Emerging Applications:

  • Minimal Residual Disease Detection: H3K36me2 patterns in circulating tumor DNA may serve as sensitive markers for disease recurrence

  • Resistance Mechanism Identification: Changes in H3K36me2 during treatment may indicate emerging resistance mechanisms

  • Pan-cancer Applicability: Investigate whether H3K36me2 biomarkers identified in one cancer type have value across multiple malignancies

By developing robust methodologies for H3K36me2 assessment in clinical samples and correlating these patterns with disease outcomes, researchers can harness the biomarker potential of this epigenetic modification to improve patient stratification and treatment selection.

What are common pitfalls in data interpretation when analyzing Di-methyl-Histone H3(K36) patterns?

• Misinterpreting the Relationship Between Methylation States:

  • Pitfall: Assuming H3K36me2 is merely an intermediate state toward H3K36me3

  • Reality: H3K36me1, H3K36me2, and H3K36me3 each represent independent chromatin states with distinct functions and genomic localizations

  • Solution: Analyze each methylation state separately and consider their unique roles in different genomic contexts

• Overlooking Genomic Context Dependency:

  • Pitfall: Treating H3K36me2 as a uniform mark across the genome

  • Reality: H3K36me2 patterns are highly context-dependent, with different methyltransferases (NSD, Set2, Ash1) establishing distinct patterns in different genomic regions

  • Solution: Segment analysis by genomic features (intergenic regions, gene bodies, heterochromatin) and consider the responsible methyltransferase for each region

• Reader Protein Dual Specificity Confusion:

  • Pitfall: Attributing reader protein recruitment solely to H3K36me3

  • Reality: Some reader proteins (e.g., MSL3, JASPer) bind both H3K36me2 and H3K36me3, creating a more complex recruitment pattern

  • Solution: Conduct methylation state-specific binding assays and consider the combined influence of both methylation states

• Normalization and Quantification Issues:

  • Pitfall: Using inappropriate normalization methods for ChIP-seq data

  • Reality: Global changes in H3K36me2 levels can confound standard normalization approaches

  • Solution: Implement spike-in normalization or use invariant genomic regions as internal controls

• Epigenetic Balance Misinterpretation:

  • Pitfall: Analyzing H3K36me2 in isolation from other histone modifications

  • Reality: Changes in H3K36me2 often affect other modifications, particularly H3K27me3, creating complex epigenetic balance effects

  • Solution: Perform integrated analysis of multiple histone modifications and consider their interdependencies

• Threshold Effects Oversight:

  • Pitfall: Expecting linear relationships between H3K36me2 levels and biological outcomes

  • Reality: Many H3K36me2-dependent processes exhibit threshold effects, where partial reduction may have minimal impact until a critical threshold is reached

  • Solution: Perform careful titration experiments and consider non-linear models when analyzing dose-response relationships

• Cell Type Heterogeneity Challenges:

  • Pitfall: Assuming homogeneous H3K36me2 patterns across cell populations

  • Reality: Cell-to-cell variation in H3K36me2 patterns can mask important biological signals in bulk analysis

  • Solution: Consider single-cell approaches when possible or validate findings in purified cell populations

• Common Statistical Analysis Errors:

By recognizing these common pitfalls and implementing appropriate analytical strategies, researchers can generate more accurate and biologically meaningful interpretations of H3K36me2 patterns in their experimental systems.

How should researchers validate the specificity of Di-methyl-Histone H3(K36) antibodies in their experimental systems?

Validating the specificity of Di-methyl-Histone H3(K36) antibodies in experimental systems requires a multi-faceted approach that addresses both technical and biological aspects of antibody performance:

• Biochemical Validation Strategy:

  • Peptide Competition Assays: Pre-incubate antibodies with increasing concentrations of H3K36me2 peptides, H3K36me1 peptides, H3K36me3 peptides, and unmodified H3K36 peptides. A specific antibody should show signal reduction only with the H3K36me2 peptide.

  • Modified Peptide Arrays: Test antibody reactivity against a comprehensive array of histone peptides with various modifications to detect any cross-reactivity with similar methylation marks.

  • Western Blot Analysis: Run recombinant histones with defined modifications alongside experimental samples to confirm single band detection at the appropriate molecular weight.

• Genetic System Controls:

  • Methyltransferase Knockdown/Knockout: Generate cells with reduced NSD expression to specifically decrease H3K36me2 while minimally affecting H3K36me3 .

  • Histone Mutant Expression: Introduce H3K36R or H3K36A mutants that cannot be methylated to create negative control samples.

  • Validation Matrix: Compare antibody signal across these genetic perturbations using multiple detection methods:

• Application-Specific Validation Protocols:

  • For ChIP/ChIP-seq:

    • Include IgG negative controls and verify enrichment at known H3K36me2-positive regions

    • Perform sequential ChIP (H3K36me2 followed by H3K36me3) to distinguish dual-marked regions

    • Compare peak distributions with published datasets and expected genomic locations (intergenic regions, weakly transcribed genes)

  • For Immunofluorescence/Immunohistochemistry:

    • Include appropriate blocking peptides as negative controls

    • Compare staining patterns with other H3K36me2 antibodies from different sources

    • Verify co-localization with expected reader proteins (e.g., yKu70/KU70)

  • For Protein Interaction Studies:

    • Perform pulldowns with synthetic H3K36me2 peptides and verify specific enrichment of known readers

    • Include appropriate washing controls to eliminate non-specific interactions

    • Validate interactions using orthogonal methods (e.g., SPR, ITC)

• Cross-Laboratory Validation:

  • Reference Sample Exchange: Share standard samples with collaborating laboratories to assess reproducibility

  • Antibody Comparison: Test multiple H3K36me2 antibodies from different vendors on the same samples

  • Orthogonal Methods: Validate key findings using antibody-independent methods when possible (e.g., mass spectrometry)

• Documentation and Reporting Standards:

  • Document complete validation data, including lot numbers and experimental conditions

  • Report all validation experiments in publications, even if relegated to supplementary materials

  • Share validation protocols with the research community to establish best practices

By implementing this comprehensive validation framework, researchers can ensure the specificity of their Di-methyl-Histone H3(K36) antibodies and generate reliable data that advances our understanding of this important epigenetic modification's biological functions.